Ziyue Qiao is currently a tenure-track Asistant Professor at School of Computing and Information Technology, Great Bay University. He was a Postdoctoral Researcher at The Hong Kong University of Science and Technology (Guangzhou), supervised by Professor Hui Xiong (IEEE Fellow and AAAS Fellow). He received his Ph.D. degree in 2022 at the Computer Network Information Center, Chinese Academy of Sciences, supervised by Professor Yuanchun Zhou. He has industrial experience in 2021 at City Brain Lab, DAMO Academy, Alibaba, as a Research Intern. He received his B.E. degree in 2017 from the School of Computing, Wuhan University.

πŸ”ˆ: I am actively seeking Postdocs, Research Assistants, and Visiting Students. Please feel free to contact me!

Research interests

His research interests encompass Data Mining, Graph Machine Learning, and Knowledge Graph. His previous work has primarily focused on the following areas:

  • Transferable Machine Learning: Pre-training & fine-tuning, Prompt learning, Out-of-distribution Learning, Continual learning.
  • Data-efficient Machine Learning: Semi-supervised, Self-supervised learning, Data-centric learning on graphic data.
  • Multimodal Learning: Multimodal alignment and fusion, Graph learning enhanced by large language models (LLMs).
  • AI for Academic Community: Academic knowledge graph, Scholar name disambiguation, Proposal review assignment, and Academic pre-trained model.

Recently, His primary research area has been Model-efficient Machine Learning. The goal is to minimize the need for training new models, instead reusing/fine-tuning existing models or leveraging foundation models/large language models for various machine learning tasks. The focus is on utilization maximization and sustainability of models/knowledge through techniques such as transfer learning, continual learning, data-centric learning. He has a special interest in applying these objectives in the field of graph machine learning.


  • πŸ“ƒ 2024.06: One paper got accepted to TKDD!
  • πŸ’ 2024.06: Invited to serve as PC members for NeurIPS 2024, AAAI 2025.
  • πŸ’ 2024.05: Invited to serve as a Reviewer for TKDD.
  • πŸ“ƒ 2024.05: Two papers got accepted to PR!
  • πŸ“ƒ 2024.04: One paper got accepted to ESWA!
  • πŸ“ƒ 2024.02: One survey paper got accepted to Neural Networks! see Deep Graph Representation Learning.
  • πŸ’ 2024.01: Invited to serve as PC members for IJCAI 2024, MM 2024, and KDD 2024.
  • πŸ“ƒ 2024.01: One paper got accepted to SCIENCE CHINA Information Sciences (SCIS), a CCF-A journal!
  • πŸ’ 2023.11: Invited to serve as Reviewers for Neural Networks and TPAMI.
  • ……


  • Best Ranked Papers of IEEE International Conference on Data Mining 2022
  • Gold Medal of Biendata Competition β€œOAG–WhoIsWho track 1” 2019 (1st Place/131 teams)
  • Beijing Outstanding Graduates Award in Beijing, China (Top 5%)
  • Presidential Scholarship at Chinese Academy of Sciences (Top 1%)
  • China Scholarship Council Scholarship for joint PhD students
  • National Scholarship (the most prestigious scholarship in China) (Top 2%)
  • Student Travel Award of IEEE International Conference on Big Data 2019

Academic Sevices

  • Session Chair of β€œMining Graphs” for IJCAI 2023 Macau
  • PC Member/Conference Reviewer: IJCAI 2023, KDD 2023, CIKM 2023, NeurIPS 2023, NeurIPS 2023 Track Datasets and Benchmarks, SDM 2024, WSDM 2024, AAAI 2024, WWW 2024, ICLR 2024, MM 2024, IJCAI 2024, KDD 2024, NeurIPS 2024, NeurIPS 2024 Track Datasets and Benchmarks, AAAI 2025
  • Journal Reviewer: IEEE TPAMI, ACM TKDD, ACM TALLIP, IEEE TLT, Neural Networks, Neurocomputing